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Random effects regression mixtures for analyzing infant habituation

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  • Derek S. Young
  • David R. Hunter

Abstract

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.

Suggested Citation

  • Derek S. Young & David R. Hunter, 2015. "Random effects regression mixtures for analyzing infant habituation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1421-1441, July.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1421-1441
    DOI: 10.1080/02664763.2014.1000272
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    Cited by:

    1. Luísa Novais & Susana Faria, 2021. "Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study," Computational Statistics, Springer, vol. 36(4), pages 2507-2533, December.
    2. Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1053-1082, December.

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